We present a method for learning multiple scene representations given a small
labeled set, by exploiting the relationships between such representations in
the form of a multi-task hypergraph. We also show how we can use the hypergraph
to improve a powerful pretrained VisTransformer model without any additional
labeled data. In our hypergraph, each node is an interpretation layer (e.g.,
depth or segmentation) of the scene. Within each hyperedge, one or several
input nodes predict the layer at the output node. Thus, each node could be an
input node in some hyperedges and an output node in others. In this way,
multiple paths can reach the same node, to form ensembles from which we obtain
robust pseudolabels, which allow self-supervised learning in the hypergraph. We
test different ensemble models and different types of hyperedges and show
superior performance to other multi-task graph models in the field. We also
introduce Dronescapes, a large video dataset captured with UAVs in different
complex real-world scenes, with multiple representations, suitable for
multi-task learning.

使用多任务超图的关系，通过一种利用标记小集合学习多个场景表示的方法，同时也展示了如何在不使用任何额外标记数据的情况下利用超图来改进强大的预训练 VisTransformer 模型。